Optimised rough sets for modelling interstate conflict

Rough set theory is technique that deals with the formal approximation of crisp sets thereby modeling vagueness and uncertainty. This paper presents an approach to optimize rough set partition sizes using various optimization techniques for interstate conflict. The four optimization techniques used are genetic algorithm, particle swarm optimization, hill climbing and simulated annealing. The results obtained from this granulization method are compared to static granulisation methods, namely, equal-width-bin and equal-frequency-bin partitioning. The results show that all of the proposed optimised methods produce higher forecasting accuracies than that of the two static methods and that genetic algorithm approach produced the highest accuracy. The rules generated from the rough set are linguistic and easy-to-interpret, but this does come at the expense of the accuracy lost in the discretisation process where the granularity of the variables is decreased.